Treatment planning for optimal clinical outcomes

Abstract

A system and method for determining an optimal clinical outcome, comprising receiving images of a skin tissue of a patient from an image capturing device; analyzing the one or more images of the skin tissue; and determining skin characteristics and physiological factors of the skin tissue based on the one or more images analyzed. The system and method further analyzing the determined skin characteristics and physiological factors of the skin tissue; determining treatment parameters based on the determined skin characteristics and physiological factors analyzed; generating skin response predictor images of predicted treatment outcomes based on a combination of the one or more images of the skin tissue and treatment parameters; scoring the skin response predictor images based on safety and predetermined criterion of efficacy; and displaying one or more treatment parameters based on the scoring of the skin response predictor images.

Description

TREATMENT PLANNING FOR OPTIMAL CLINICAL OUTCOMESRELATED APPLICATIONS[1] This application is related to US Provisional Application No. 63 / 533,737 filed 21 August 2023, entitled “TREATMENT PLANNING FOR OPTIMAL CLINICAL OUTCOMES,” to which application priority is hereby claimed and the contents of which are incorporated herein by reference in its entirety.FIELD OF INVENTION[2] The present disclosure is in the medical field and relates to aesthetic treatment techniques, more particularly, but not exclusively, to treatment planning for optimal clinical outcomes.BACKGROUND[3] Many of the therapeutic and aesthetic skin treatments are energy -based treatments involving light, laser, LED, Intense Pulsed Light (IPL), RF or ultrasound. Such treatments are typically used for performing selective photo-thermolysis for clinical indications such as photo-aging, telangiectasia, rosacea, hair removal, acne treatment, etc. The efficacy and safety of the skin treatment depends on proper setting of the treating energy or energy treatment parameters. An improper setting of the energy treatment parameters can, however, cause an irreparable damage to a skin tissue.[4] A discussion on a method and system for determining optimal parameters for operating an aesthetic skin treatment is found in WO 2021 / 186443 Al assigned to the assignee of the present invention, the disclosure of which is incorporated herein by reference in its entirety.[5] The information disclosed in this background of the disclosure section is for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.SUMMARY[6] The present disclosure relates to a skin treatment technique utilizing target tissue survivability maps. The target tissue survivability maps refer to tissue coagulation or damage probability maps, which are determined from one or more images of a skin tissue of a patient.[7] Prior to actual skin treatment on a patient, the present disclosure enables and allows a physician to view one or more probable treatment clinical outcomes based on analysis of a personal target tissue survivability maps corresponding to treatment parameters. Based on physician’s assessment, the physician can select one of the treatment clinical outcomes as the desired treatment outcome to a specific patient and by this determine the treatment parameter to be applied. Additionally, or alternatively, the physician can fine tune (adjust) treatment parameters to obtain an optimal treatment outcome or an optimal skin treatment for a patient based on his personal target tissue characteristics and clinical indications.[8] In another scenario, enabled by the present invention, an optimal treatment outcome is presented to the physician who then may give approval for applying the treatment parameter corresponding to the optimal treatment outcome. Determination of the optimal treatment outcome is determined internally by a system of the invention that analyzes and compares different treatment outcomes by generating, processing and analyzing corresponding survivability maps of the different treatment outcomes.[9] In accordance with the invention, survivability maps, possibly at different stages of a skin treatment, may be presented to the physician together with the corresponding treatment clinical outcomes.

[0010] In an embodiment, a method for determining an optimal clinical outcome is disclosed. The method comprises receiving one or more multispectral images of a skin tissue of a patient from an image capturing device. Thereafter, the method comprises analyzing the multispectral images of the skin tissue and generating one or more target tissue survivability maps corresponding to one or more treatment parameter(s) / protocol(s). Lastly, the method comprises displaying one or more treatment outcomes corresponding to the one or more treatment parameters based on the one or more target tissue survivability maps, to enable a physician to decide on skin tissue treatment.

[0011] In another embodiment, a system for determining an optimal clinical outcome is disclosed. The system comprises a processor and a memory communicatively coupled to the processor, wherein the memory stores processor executable instructions, which, on execution, cause the processor to receive multispectral images of a skin tissue of a patient from an image capturing device. Thereafter, the processor is configured to analyze the multispectral images of the skin tissue and generate one or more target tissue survivability maps corresponding to respective one or more treatment parameter(s) / protocol(s). Lastly, the processor is configured to display one or more treatment outcomes corresponding tothe one or more treatment parameters based on the one or more target tissue survivability maps to enable a physician to decide on skin tissue treatment.

[0012] In some embodiments, prior to receiving the multispectral images of the skin tissue of the patient, the method comprises imaging the skin tissue of the patient.

[0013] In some embodiments, to generate the one or more target tissue survivability maps, the method comprises processing one or more spectral images of the skin tissue, the processing includes generating a 3-dimensional skin tissue map, a skin tissue feature classification, and skin tissue physiological factors estimation.

[0014] In some embodiments, the 3-dimensional skin tissue mapping comprises an epidermal melanin concentration map, 3 -dimensional temperature map and an upper dermal hemoglobin concentration map.

[0015] In some embodiments, the skin tissue physiological factors estimation comprises estimating at least one of a skin type, a hair density, and a vascular density of the target skin tissue.

[0016] In another embodiment, treatment parameters are determined upon desired optimal treatment clinical outcome. In this aspect, multispectral images are received and analyzed; based on the analysis, one or more optimal / desired target tissue survivability maps are generated; the system solves an inverse problem and computes one or more treatment programs / plans / parameters that achieve one or more treatment clinical outcomes corresponding to the optimal target tissue survivability maps or to realistic target tissue survivability maps with an outcome close to or at the optimal; the one or more treatment clinical outcomes and the respective one or more treatment programs / plans / parameters are displayed to the physician enabling the physician to choose the treatment parameter or adjust it. If the physician adjusts one or more parameters, the system computes the corresponding treatment clinical outcome by generating respective target tissue temperature and survivability maps and presents the corresponding treatment clinical outcome to the physician who can decide whether to run the adjusted treatment parameter.

[0017] In some embodiments, there is a system for determining an optimal clinical outcome, the system comprising: a processor; and a memory communicatively coupled to the processor. The memory stores processor executable instructions, which, on execution, cause the processor to: receive multispectral images of a skin tissue of a patient from an image capturing device; analyze the multispectral images of the skin tissue; determine skin characteristics and physiological factors of the skin tissue based on the multispectral images analyzed; analyze the determined skin characteristics and physiological factors of the skintissue; determine treatment parameters based on the determined skin characteristics and physiological factors analyzed; generate skin response predictor images of predicted treatment outcomes based on a combination of the multispectral images of the skin tissue and treatment parameters; score the skin response predictor images based on safety and predetermined criterion of efficacy; and display one or more treatment parameters based on the scoring of the skin response predictor images.

[0018] In some embodiments, the system further comprises a skin response predictor module that with the processor is configured to generate the skin response predictor images using a skin response predictor model. In some embodiments, the skin response predictor module is pretrained with clinical data of outcomes for skin tissue of a plurality of energy treatments. In some embodiments, the skin characteristics and physiological factors of the skin tissue are at least one of the following: chromophore; pigment, blood vessel depth; blood vessel diameter, hair identification, hair diameter; hair color, clinical diagnosis; acne; scars; striae lines; or skin type identification.

[0019] In some embodiments, the system further comprises a survivability module, that with the processor is configured to: generate one or more target tissue survivability maps corresponding to one or more treatment parameters and determine the treatment parameters based on the determined skin characteristics and physiological factors analyzed. In some embodiments, the system further comprises a survivability module, that with the processor is configured to determine, based on an algorithm, the treatment parameters based on the determined skin characteristics and physiological factors analyzed. The system comprises a scoring module that with the processor is configured to: compare skin response predictor images to the received multispectral images of a skin tissue for efficacy; and score the comparisons based on meeting the predetermined criteria of efficacy and safety.

[0020] In some embodiments, there is a system for determining an optimal clinical outcome, the system comprising: a processor; and a memory communicatively coupled to the processor. The memory stores processor executable instructions, which, on execution, cause the processor to: receive multispectral images of a skin tissue of a patient from an image capturing device; analyze the multispectral images of the skin tissue; generate one or more target tissue survivability maps corresponding to one or more treatment parameters; and display one or more treatment clinical outcomes based on the one or more target tissue survivability maps to enable a physician to decide on a skin treatment. In some embodiments, the processor is configured to analyse the multispectral images of the skin tissue for generating one or more target tissue survivability maps, by: calculating andgenerating a 3-dimensional (3D) chromophore skin tissue map; generating a skin tissue feature classification; calculating the heat diffusion of the skin based on the 3D chromophore skin tissue map and the skin tissue feature classification; generating a temperature map of the calculated heat diffusion of the skin with corresponding one or more treatment parameters; and generating the one or more target tissue survivability maps based on the temperature map.

[0021] In some embodiments, the the processor is configured to generate the temperature maps based on at least one predetermined treatment and by: correlating the predetermined treatment with the skin tissue feature classification and desired clinical indication of the predetermined treatments of the skin.

[0022] In some embodiments, there is a method for determining an optimal clinical outcome, the method comprising: receiving multispectral images of a skin tissue of a patient from an image capturing device; analyzing the multispectral images of the skin tissue; determining skin characteristics and physiological factors of the skin tissue based on the multispectral images of the skin tissue analyzed; analyzing the determined skin characteristics and physiological factors of the skin tissue; generating treatment parameters based partially on the determined skin on the characteristics and physiological factors analyzed; generating skin response predictor images of predicted treatment outcomes based on a combination of the multispectral images of the skin tissue and treatment parameters; scoring the skin response predictor images based on safety and predetermined criterion of efficacy; and displaying one or more treatment parameters based on the scoring of the skin response predictor images.

[0023] In some embodiments, the method further comprises providing a skin response predictor module and a processor configured to generate the skin response predictor images, and wherein the skin response predictor module comprises a skin response predictor model. Also, the skin response predictor module is pretrained with clinical data of outcomes for skin tissue of a plurality of energy treatments. In some embodiments, the skin characteristics and physiological factors of the skin tissue are at least one of the following: chromophore; pigment, blood vessel depth; blood vessel diameter, hair identification, hair diameter; hair color, clinical diagnosis; acne; scars; striae lines; or skin type identification.

[0024] In some embodiments, the method further comprises providing a survivability module and a processor configured to generate one or more target tissue survivability maps corresponding to one or more treatment parameters and determining the treatmentparameters based on the determined skin characteristics and physiological factors analyzed and the survivability maps generated. In some embodiments, the method further comprises providing a survivability module and a processor, wherein the survivability module employs an algorithm; and determining, by the survivability module and the processor, based on an algorithm, the treatment parameters based on the determined skin characteristics and physiological factors analyzed.

[0025] In some embodiments, the method further comprises providing a scoring module and a processor: comparing, by the scoring module and processor, skin response predictor images to the received multispectral images of a skin tissue for efficacy; and scoring the comparisons based on meeting the predetermined criteria of efficacy and safety.

[0026] Embodiments of the present disclosure according to the above-described treatment planning method may bring about several advantages.

[0027] The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description.BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Fig. 1 illustrates a flowchart showing a method for determining an optimal clinical outcome by utilizing tissue survivability maps, for use in treatment decision, in accordance with some non-limiting embodiments of the present disclosure.

[0029] Fig. 2 illustrates ways of analyzing skin images to determine the target tissue survivability maps, in accordance with some non-limiting embodiments of the present disclosure.

[0030] Fig. 3 shows a block diagram of a system for determining an optimal clinical outcome by utilizing target tissue survivability maps, for use in treatment decision, in accordance with some non-limiting embodiments of the present disclosure.

[0031] Figs. 4a and 4b illustrate a non-limiting example for determining target tissue survivability maps in accordance with an embodiment of the present disclosure.

[0032] Fig. 5 illustrates another non-limiting example for determining target tissue survivability maps in accordance with an embodiment of the present disclosure.

[0033] Fig. 6 illustrates another non-limiting example for determining treatment planning for optimal clinical outcomes in accordance with an embodiment of the present disclosure.DETAILED DESCRIPTION

[0034] In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings that form a part hereof, and in which are shown by way of illustration specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.

[0035] Fig. 1 illustrates a flowchart showing a method for use in determining a treatment parameter, treatment planning and decision, by utilizing target tissue survivability maps, in accordance with some embodiments of the present disclosure.

[0036] In some embodiments, the method includes receiving one or more images of a skin tissue of a patient, e.g. from an image capturing device, at step 101. The image capturing device may, also, be referred to as a skin diagnostics hardware. In some embodiments, the one or more images are multispectral images. The multispectral images may be multiple images captured with a plurality of illumination lights and each illumination light is of a different peak wavelength. In some embodiment, the method includes imaging the skin tissue of the patient prior to receiving the multispectral images of the skin tissue of the patient. Thereafter, the method includes analyzing the multispectral images of the skin tissue and generating one or more target tissue survivability maps corresponding to one or more suggested treatment parameters, at step 103.

[0037] The target tissue survivability map(s) may be indicative of a target tissue damaging or coagulating probability map(s), where the target tissue damaging or coagulating is caused by treating the target tissue with an energy-based treatment devices a such as light, laser, IPL, LED, radiofrequency (RF), ultrasound, microwave that causes the target tissue to heat. In some embodiments, the method includes displaying one or more treatment clinical outcomes corresponding respectively to the one or more treatment parameters, target tissue or clinical indication, based on the one or more target tissue survivability maps obtained, to enable a physician to decide on a skin treatment, at step 105.

[0038] In some embodiments, the method includes determining a subset of optimal treatment parameters of the one or more treatment parameters to the physician and displaying the subset of survivability maps with treatment parameters. In one example, the one or more target tissue survivability maps may be displayed to the physician, in addition to the treatment clinical outcomes, and the physician may assess the one or more target tissuesurvivability maps in determining the treatment parameter to be applied. Based on the assessment, the physician can select one or more of the target tissue survivability maps from the displayed one or more target tissue survivability maps or fine tune (adjust) treatment parameters to obtain an optimal survivability map for an optimal skin treatment.

[0039] Fig. 2 illustrates exemplary embodiment of analysis of the skin images to determine treatment planning for optimal clinical outcomes with target tissue survivability maps.

[0040] As shown, multispectral images 301 refer to image(s) of a skin tissue of a patient, received at step 101 as described above. The multispectral images 301 of the skin tissue of the patient are captured or imaged by an image capturing device configured for obtaining multispectral images of the skin. At step 103 described above, the multispectral images 301 of the skin tissue are analyzed and processed for obtaining 3-dimensional skin tissue chromophore maps 303, a skin tissue feature classification 305, and skin tissue physiological factors estimation, as shown further below in Fig. 4a. As shown, in this nonlimiting example, a 3-dimensional skin tissue chromophore mapping 303 may include an epidermal melanin concentration map and an upper dermal hemoglobin concentration map. The skin tissue physiological factors estimation may include at least one of a skin type, a hair density, and a vascular density of the target skin tissue, as shown further below in Fig. 4a. Thereafter, a heat diffusion model is applied and solved under each proposed skin treatment plan / parameter 319 (which is hypothetically applied to the skin) and temperature maps 320 are obtained. Survivability maps 322 indicative of the theoretical thermal damage caused over time under the specific treatment parameter ranges can be described using a first-order bimolecular reaction kinetics model in which the state of the skin tissue is either viable (7) or dead (£)) according to

[0041] where k is a reaction rate that can be calculated using an Arrhenius model, k = Ae~Ea^RT(2)

[0042] where Eais an activation energy, A is a frequency factor, R is a gas constant and T is a temperature. Both the activation energy and the frequency factor are target tissue dependent experimental parameters that can be experimentally determined.

[0043] Then, the heat induced fractional tissue survival S, can be derived as:

[0044] Where S = 1 means that the target skin tissue is viable and alive and S = 0 means that the target skin tissue underwent unreversible changes.

[0045] Fig. 3 shows a detailed block diagram of a system for treatment planning utilizing target tissue survivability maps and / or target tissue survivability data obtained as described above, in accordance with some embodiments of the present disclosure.

[0046] The method for determining skin tissue treatment planning utilizing target tissue survivability data, mentioned above in Fig. 2, may be implemented using a system 200 shown in Fig. 3. The system 200 of the present disclosure includes an Input / Output (I / O) interface 201, a processor 203, and a memory 205. In this disclosure, “processor” (in the singular) is understood to mean one or more processors that may be hosted on a single computer or whether the features and functions of the processor are distributed over a plurality of networked computers.

[0047] The I / O interface 201 may include communication protocols / methods such as, without limitation, audio, analog, digital, monaural, Radio Corporation of America (RCA) connector, stereo, IEEE® 1394 high speed serial bus, serial bus, Universal Serial Bus (USB), infrared, Personal System / 2 (PS / 2) port, Bayonet Neill Concelman (BNC) connector, coaxial, component, composite, Digital Visual Interface (DVI), High Definition Multimedia Interface (HDMI®), Radio Frequency (RF) antennas, S Video, Video Graphics Array (VGA), IEEE® 802.1 Ib / g / n / x, Bluetooth, cellular e.g., Code Division Multiple Access (CDMA), High Speed Packet Access (HSPA+), Global System for Mobile communications (GSM®), Long Term Evolution (LTE®), Worldwide interoperability for Microwave access (WiMax®), Dedicated Short-Range Communications (DSRC), or the like. In some embodiments, the processor 203 further comprises associated user interfaces through the I / O interface 201 including but not limited to a display or an input device which may include a keyboard and / or mouse (not shown).

[0048] The system 200 receives multispectral images of a skin tissue of a patient, collected by an image capturing device, through the I / O interface 201.

[0049] The memory 205 is communicatively coupled to the processor 203 of the system 200. The memory 205, also, stores processor instructions which cause the processor 203 to execute the instructions for determining a skin tissue treatment plan.

[0050] The processor 203 includes at least one data processor for determining the skin tissue treatment plan.

[0051] The system 200, in addition to the I / O interface 201 and the processor 203 described above, includes data 207 and one or more modules 213. In the described embodiment, the data 207 is stored within the memory 205. The data 207 includes, for example, image data 209, and optionally historical clinical data 211.

[0052] In some embodiments, the image capturing device configured for providing the image data 209 is part of the system 200 (not shown in Fig. 2). The image data 209 includes, but not limited to, one or more multispectral images of a skin tissue of a patient. In case the image capturing device is external to (i.e., not part of) the system 200, the communication between the system 200 and the image capturing device can be using, but is not limited to, wired network, an e-commerce network, a Peer to Peer (P2P) network, Local Area Network (LAN), Wide Area Network (WAN), wireless network (for example, using Wireless Application Protocol), Internet, Wi-Fi, Bluetooth, Code Division Multiple Access (CDMA), High Speed Packet Access (HSPA+), Global System for Mobile communications (GSM®), Long Term Evolution (LTE®), Worldwide interoperability for Microwave access (WiMax®), Dedicated Short-Range Communications (DSRC), or the like.

[0053] The historical clinical data 211 includes, but not limited to, clinical data of past patients. The clinical data may include multispectral images of a skin tissue of patients and respective target tissue survivability maps corresponding to one or more treatment parameters.

[0054] In the described embodiment, the data 207 in the memory 205 is processed by the one or more modules 213 present within the memory 205 of the system 200. In some embodiments, the one or more modules 213 may be implemented as dedicated hardware units. As used herein, the term module refers to an Application Specific Integrated Circuit (ASIC), an electronic circuit, a Field Programmable Gate Arrays (FPGA), Programmable System on Chip (PSoC), a combinational logic circuit, and / or other suitable components that provide the described functionality. In some implementations, the one or more modules 213 are communicatively coupled to the processor 203 for performing one or more functions of the system 200. The one or more modules 213 when configured with the functionality defined in the present disclosure will result in a novel hardware.

[0055] In one implementation, the one or more modules 213 include, but are not limited to, a receiving module 215, an analyzing module 217, and a displaying module 219.

[0056] The receiving module 215 receives multispectral images of a skin tissue of a patient. In some embodiment, the multispectral images are received directly from an image capturing device connected or included in the system 200.

[0057] In some embodiments, analyzing module 217 analyzes the multispectral images of the skin tissue for generating one or more target tissue survivability maps and / or data based on proposed one or more treatment parameters. In some embodiments, analyzing module outputs skin characteristics and physiological factors. In some embodiments, the analyzing module 217 processes the multispectral images of the skin tissue for calculation of 3- dimensional skin chromophore map, classifying skin tissue features based on the 3D chromophore maps, and skin tissue physiological factors estimation. The 3-dimensional skin chromophore mapping may comprise an epidermal melanin concentration map and an upper dermal hemoglobin concentration map. In some embodiments, the skin tissue physiological factors estimation comprises, but is not limited to, estimating at least one of a skin type, a hair density, and a vascular density, melanin or other pigments or chromophores densities of the target skin tissue. Thereafter, by analyzing heat transfer and diffusion as described in reference to Fig. 2, the analyzing module 217 generates one or more temperature maps based on the skin tissue processed images and one or more suggested treatment plans / parameters (e.g. treatment pulse sequences, including pulse wavelength, fluence and temporal shape). The temperature maps may be 2-dimensional presenting the temperature in different and various skin layers in the 3 -dimensional skin mapping.

[0058] Based on the temperature map(s), the analyzing module 217 determines one or more target tissue survivability maps. In some embodiments, a target tissue survivability map and / or data is determined and / or generated for each skin tissue layer or each skin tissue constituent / feature. In some embodiments, a composite target tissue survivability map is generated, describing tissue effects in a plurality of layers and / or for a plurality of tissue constituents / features. In some embodiments, a 3-dimensional target tissue survivability map is generated for a volume of tissue.

[0059] The analyzing module 217 may use artificial intelligence such as, but not limited to, machine learning and deep learning to determine the one or more target tissue survivability maps. In some embodiments, the analyzing module 217 with artificial intelligence is trained on image data collected in clinical trials. The artificial intelligence may be a decision tree learning algorithm and / or reaction rate theory model.

[0060] In some embodiments, the analyzing module 217, based on physician’s input (about treatment parameters or treatment outcome), adjusts the one or more target tissue survivability maps to obtain an optimal survivability map to be used in displaying the corresponding treatment clinical outcome.

[0061] The displaying module 219 displays the one or more treatment clinical outcomes, and optionally the respective target tissue survivability maps, to enable a physician to make decision on skin treatment. The displaying module 219 may be, but not limited to, a mobile device, a computer display or a laptop display.

[0062] Figs. 4a and 4b illustrate non-limiting examples for treatment planning by using target tissue survivability maps in accordance with some embodiments of the present disclosure.

[0063] The system 200 receives the multispectral images 301 of the skin tissue of the patient from the image capturing device and processes the multispectral images 301 of the skin tissue for a 3-dimensional skin tissue chromophore mapping, a skin tissue feature classification, and skin tissue physiological factors estimation. The images 303 refer to the output of system 200 for the skin tissue feature classification. The images 305 refer to the output of system 200 for the 3-dimensional skin tissue chromophore mapping. The 3- dimensional skin tissue mapping may include an epidermal melanin concentration map and an upper dermal hemoglobin concentration map. For the skin tissue physiological factors estimation, the system 200 estimates at least one of a skin type, a hair density, and a vascular density of the target skin tissue. Thereafter, the system 200 generates optimal temperature and survivability maps 307 for the different targets, such as epidermal melanin and upper dermal blood vessels, based on the skin tissue feature classification and desired clinical indication. It is noted that the target optimal temperature and survivability maps 307 are generated based on desired / optimal clinical outcome and regardless of a treatment plan / parameter. As shown, the target optimal survivability maps 307 include a first map for the optimal survivability of the epidermal melanin and a second map for the optimal survivability of the upper dermal blood vessels.

[0064] Thereafter, the system 200 may solve the inverse problem 309 to find the optimal treatment parameter (which may be a pulse sequence) 311 and its corresponding realistic optimal survivability map(s) by applying, but not limited to, a decision tree learning algorithm. The realistic optimal survivability maps may refer to the immediate response resulting from application of the specific treatment parameter. Subsequently, the system 200 displays the one or more realistic optimal target tissue survivability maps 311 and the corresponding treatment parameters to enable a physician to make decision on the treatmentplan to apply. At this point, the physician may assess the one or more target tissue survivability maps 311 and / or corresponding treatment pulse sequences for an optimal clinical outcome. The physician may select an optimal survivability map (step 313) from the one or more target tissue survivability maps (step 311) and the treatment is carried out. Alternatively, the physician may adjust the treatment parameters (step 315) and the system recalculates the survivability maps (step 311) expected from treatment by the adjusted parameters and displays the adjusted survivability maps to the physician. The physician may then approve or adjust the treatment parameters again and further survivability maps are generated by the system until the physician is satisfied with the expected outcome and the treatment is executed.

[0065] In Fig. 4b, illustrates how different treatments may lead to different survivability maps and thus, this may lead to different clinical outcomes. Two such scenarios, by way of specific example, are shown in Fig. 4b. For pulse sequence A 321, a high contrast pigment is estimated to undergo irreversible changes, which is shown by temperature maps 325, while the vascular network of the upper dermis and the dermal collagen remain intact, which is shown by target tissue survivability maps 327. For the same skin treatment area with a different treatment pulse sequence B 323, a reduced survivability of the vascular network in the upper dermis together with an impact on the epidermal melanin is seen, reference 331. This approach allows the physician to ensure proper setting of the energy treatment parameters prior to actual skin treatment to prevent any irreparable damage to the skin tissue.

[0066] Prior to using the system 200 for determining target tissue survivability maps for deciding on an optimal clinical outcome, the system 200 may be trained (e.g., machine learning / Artificial intelligence techniques) by utilizing clinical data. The training will allow to calibrate the system to estimate survivability maps for arbitrary treatment plans / programs / pulses. Several examples, similar to the examples shown in Fig. 4b, utilizing clinical data of before and after treatment with different pulses can be used to train and calibrate the system.

[0067] In some embodiments, system 200 is trained to obtain target tissue survivability maps based on the correlation of the 3-dimensional skin tissue mapping, the skin tissue feature classification, and temperature maps of the skin for different energy treatments such as different lasers or IPL. Following the training of the system 200, the system 200 can generate one or more target tissue survivability maps using the multispectral images of the skin tissue in real-time.

[0068] Fig. 5 illustrates another non-limiting example for treatment planning utilizing target tissue survivability maps, in accordance with an embodiment of the present disclosure.

[0069] In some embodiments, the system 200 receives multispectral images of a skin tissue 401 of a patient from an image capturing device. Thereafter, the system may process the multispectral images of the target skin tissue to generate the skin tissue feature classification maps (melanin, blood vessels, ...) in the different skin layers (epidermal, upper dermal, ...). The system may then present to the physician several options. For example, in option A 403, the desired clinical outcome is to treat the blood capillaries in the upper dermal skin layer while not treat organs / features in the epidermal melanin skin layer. In option B, the desired clinical outcome is to treat lentigines in the epidermal melanin skin layer while not treating organs / features in the upper dermal skin layer. The physician may choose option A or option B. Then, the system may calculate the one or more target tissue survivability maps 407 at the two skin layers, for the selected option, based on one or more treatment plans, and displays the treatment clinical outcomes to the physician who can choose, at 409, the best / optimal outcome in the two skin layers. Alternatively, the physician may adjust the treatment parameters (the treatment plan), at 411, and the system recalculates the survivability maps corresponding to the adjusted treatment plan to enable the physician to estimate the outcome of the adjusted treatment plan before running the treatment plan.

[0070] The present disclosure allows a physician to view treatment clinical outcome maps before performing the actual skin treatment. This approach allows the physician to ensure proper setting of the energy treatment parameters.

[0071] The present disclosure allows the physician to adjust the treatment parameters to obtain an optimal clinical outcome for the skin treatment. This approach prevents any irreparable damage to a skin tissue of a patient.

[0072] Fig. 6 illustrates another non-limiting example for treatment planning utilizing target tissue survivability indicators, in accordance with some embodiment of the present disclosure.

[0073] In some embodiments, the system 200 receives multispectral images of a skin tissue 401 as shown in Fig. 5 of a patient from an image capturing device. Thereafter, the system may process the multispectral images of the target skin tissue to generate the skin tissue feature classification maps (melanin, blood vessels, ...) in the different skin layers (epidermal, upper dermal, ...). In some embodiments, a skin diagnostics module (not shown) in the analyzing module 217 receives the one or more multi-spectral skin image ofa desired treatment area of the skin tissue as an input and delivers as an output skin characteristics and physiological factors. In some embodiments, the skin characteristics and physiological factors comprise but are not limited to the following: 3D chromophore mapping, pigment mapping, blood vessel depth and diameter mapping, hair identification, hair diameter and color mapping, mapping of clinical diagnosis such as but not limited to, Lentigenies, Melasma, Poikiloderma, Rosecea, Telangentasia, acne, Nevus of Becker and Ota, and Hemosederin.

[0074] In some embodiments, the list of skin characteristics and physiological factors as outputs from the skin diagnostic module are inputs to a survivability module 603 which is part of the analysis module 217. The survivability module may be a treatment parameters optimization algorithm developed from the method referenced by Fig. 2 and may output treatment parameters. In some embodiments, the treatment parameters may be a list of constraints of the treatment energy, such as, but not limited to fluence, spectral profile and temporal profile. In some embodiments, table 605 is an example of treatment parameters constrains limited by the survivability module outputs. In some embodiments, there is a skin response predictor 607 with a neural network or artificial intelligence to generate artificial multispectral images of predicted treatment outcomes using a combination of a real image and treatment parameters as an input.

[0075] In some embodiments, a scoring module 609, as part of analysis module 217, analyzes the artificially generated image of the predicted treatment module (skin response predictor images) in comparison to the images of a skin tissue of a patient received from an image capturing device in terms of both optimal outcome (efficacy) and safety. In some embodiments, the scoring module scores the efficacy for an outcome of predetermined criterion, such as but not limited to, homogenous skin color; reduction of legion contrast; reduction of general erythema, reduction of blood vessels. In some embodiments, the scoring function module scores based on predetermined criterion, The system may then present one or several options 611 to the physician.

[0076] In some embodiments, a rule-based module is used as part of the system. In some embodiments, the rule-based module is employed instead of the score function module and / or the skin response predictor.

[0077] In the present document, the words “energy treatment” or “treatment parameters” are used herein to also includes “ranges of treatment parameters” and “ranges of energy treatment.”

[0078] In the present document, the word "exemplary" is used herein to mean "serving as an example, instance, or illustration." Any embodiment or implementation of the present subject matter described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.

[0079] The terms “comprises”, “comprising”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a mechanism that comprises a list of components does not include only those components but may include other components not expressly listed or inherent to such mechanism. In other words, one or more elements in the device or mechanism preceded by “comprises... a” does not, without more constraints, preclude the existence of other elements or additional elements in the mechanism.

[0080] With respect to the use of substantially any plural and / or singular terms herein, those having skill in the art can translate from the plural to the singular and / or from the singular to the plural as is appropriate to the context and / or application. The various singular / plural permutations may be expressly set forth herein for sake of clarity.

[0081] Various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purposes of illustration and are not intended to be limiting.

Claims

What is claimed is:

1. A system for determining an optimal clinical outcome, the system comprising: a processor; and a memory communicatively coupled to the processor, wherein the memory stores processor executable instructions, which, on execution, cause the processor to: receive multispectral images of a skin tissue of a patient from an image capturing device; analyze the multispectral images of the skin tissue; determine skin characteristics and physiological factors of the skin tissue based on the multispectral images analyzed; analyze the determined skin characteristics and physiological factors of the skin tissue; determine treatment parameters based on the determined skin characteristics and physiological factors analyzed; generate skin response predictor images of predicted treatment outcomes based on a combination of the multispectral images of the skin tissue and treatment parameters; score the skin response predictor images based on safety and predetermined criterion of efficacy; and display one or more treatment parameters based on the scoring of the skin response predictor images.

2. The system of claim 1, wherein the system further comprises a skin response predictor module that with the processor is configured to generate the skin response predictor images using a skin response predictor model.

3. The system of claim 2, wherein the skin response predictor module is pretrained with clinical data of outcomes for skin tissue of a plurality of energy treatments.

4. The system of claim 1, wherein skin characteristics and physiological factors of the skin tissue are at least one of the following: chromophore; pigment, blood vessel depth; blood vessel diameter, hair identification, hair diameter; hair color, clinical diagnosis; acne; scars; striae lines; or skin type identification.

5. The system of claim 1, wherein the system further comprises a survivability module, that with the processor is configured to: generate one or more target tissue survivability maps corresponding to one or more treatment parameters and determine the treatment parameters based on the determined skin characteristics and physiological factors analyzed.

6. The system of claim 1, wherein the system further comprises a survivability module, that with the processor is configured to determine, based on an algorithm, the treatment parameters based on the determined skin characteristics and physiological factors analyzed.

7. The system of claim 1, wherein the system comprises a scoring module that with the processor is configured to: compare skin response predictor images to the received multispectral images of a skin tissue for efficacy; and score the comparisons based on meeting the predetermined criteria of efficacy and safety.

8. A system for determining an optimal clinical outcome, the system comprising: a processor; and a memory communicatively coupled to the processor, wherein the memory stores processor executable instructions, which, on execution, cause the processor to: receive multispectral images of a skin tissue of a patient from an image capturing device; analyze the multispectral images of the skin tissue; generate one or more target tissue survivability maps corresponding to one or more treatment parameters; and display one or more treatment clinical outcomes based on the one or more target tissue survivability maps to enable a physician to decide on a skin treatment.

9. The system of claim 8, wherein the processor is configured to analyse the multispectral images of the skin tissue for generating one or more target tissue survivability maps, by: calculating and generating a 3-dimensional (3D) chromophore skin tissue map;generating a skin tissue feature classification; calculating the heat diffusion of the skin based on the 3D chromophore skin tissue map and the skin tissue feature classification; generating a temperature map of the calculated heat diffusion of the skin with corresponding one or more treatment parameters; and generating the one or more target tissue survivability maps based on the temperature map.

10. The system of claim 9, wherein the processor is configured to generate the temperature maps based on at least one predetermined treatment and by: correlating the predetermined treatment with the skin tissue feature classification and desired clinical indication of the predetermined treatments of the skin.

11. A method for determining an optimal clinical outcome, the method comprising: receiving multispectral images of a skin tissue of a patient from an image capturing device; analyzing the multispectral images of the skin tissue; determining skin characteristics and physiological factors of the skin tissue based on the multispectral images of the skin tissue analyzed; analyzing the determined skin characteristics and physiological factors of the skin tissue; generating treatment parameters based partially on the determined skin on the characteristics and physiological factors analyzed; generating skin response predictor images of predicted treatment outcomes based on a combination of the multispectral images of the skin tissue and treatment parameters; scoring the skin response predictor images based on safety and predetermined criterion of efficacy; and displaying one or more treatment parameters based on the scoring of the skin response predictor images.

12. The method of claim 11, further comprising: providing a skin response predictor module and a processor configured to generate the skin response predictor images, and wherein the skin response predictor module comprises a skin response predictor model.

13. The method of claim 12, wherein the skin response predictor module is pretrained with clinical data of outcomes for skin tissue of a plurality of energy treatments.

14. The method of claim 11, wherein skin characteristics and physiological factors of the skin tissue are at least one of the following: chromophore; pigment, blood vessel depth; blood vessel diameter, hair identification, hair diameter; hair color, clinical diagnosis; acne; scars; striae lines; or skin type identification.

15. The method of claim 11, further comprising: providing a survivability module and a processor configured to generate one or more target tissue survivability maps corresponding to one or more treatment parameters and determining the treatment parameters based on the determined skin characteristics and physiological factors analyzed and the survivability maps generated.

16. The method of claim 11, further comprising: providing a survivability module and a processor, wherein the survivability module employs an algorithm; and determining, by the survivability module and the processor, based on an algorithm, the treatment parameters based on the determined skin characteristics and physiological factors analyzed.

17. The method of claim 11, further comprising: providing a scoring module and a processor: comparing, by the scoring module and processor, skin response predictor images to the received multispectral images of a skin tissue for efficacy; and scoring the comparisons based on meeting the predetermined criteria of efficacy and safety.

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